Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,981
2	New Jersey	18,813
3	Massachusetts	15,334
4	Rhode Island	15,191
5	District of Columbia	13,884
6	Connecticut	12,687
7	Delaware	10,618
8	Illinois	10,594
9	Maryland	10,363
10	Louisiana	10,171
11	Nebraska	8,715
12	Iowa	7,641
13	South Dakota	6,700
14	Mississippi	6,652
15	Michigan	6,638
16	Pennsylvania	6,537
17	Virginia	6,430
18	Indiana	6,152
19	Minnesota	5,447
20	Alabama	5,358
21	Georgia	5,227
22	Colorado	5,085
23	Arizona	5,084
24	New Mexico	4,695
25	Utah	4,602
26	Tennessee	4,531
27	North Carolina	4,315
28	Arkansas	4,280
29	North Dakota	4,073
30	Wisconsin	3,962
31	Kansas	3,952
32	California	3,938
33	New Hampshire	3,930
34	South Carolina	3,763
35	Nevada	3,673
36	Washington	3,621
37	Florida	3,599
38	Ohio	3,556
39	Texas	3,163
40	Kentucky	2,879
41	Missouri	2,722
42	Oklahoma	2,127
43	Maine	2,090
44	Idaho	1,942
45	Wyoming	1,864
46	Puerto Rico	1,844
47	Vermont	1,807
48	Oregon	1,381
49	West Virginia	1,295
50	Alaska	1,008
51	Montana	569
52	Hawaii	511

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	175
2	Alabama	174
3	Louisiana	156
4	Arkansas	151
5	South Carolina	142
6	North Carolina	120
7	Utah	111
8	Florida	98
9	Tennessee	84
10	Mississippi	79
11	Maryland	76
12	Georgia	72
13	California	70
14	South Dakota	70
15	Iowa	69
16	District of Columbia	68
17	Nevada	66
18	Texas	65
19	Virginia	65
20	Indiana	64
21	Nebraska	59
22	Delaware	57
23	Minnesota	53
24	Connecticut	51
25	New Mexico	50
26	Oklahoma	47
27	Illinois	46
28	Rhode Island	45
29	Washington	43
30	Kansas	41
31	Wisconsin	41
32	Kentucky	38
33	New York	38
34	North Dakota	37
35	Puerto Rico	36
36	New Jersey	35
37	Oregon	34
38	Missouri	32
39	Ohio	32
40	Massachusetts	30
41	Wyoming	29
42	Pennsylvania	28
43	Colorado	27
44	New Hampshire	23
45	Alaska	22
46	Idaho	22
47	Maine	22
48	Michigan	14
49	West Virginia	13
50	Montana	11
51	Hawaii	7
52	Vermont	4

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,575
2	New Jersey	1,427
3	Connecticut	1,179
4	Massachusetts	1,109
5	Rhode Island	803
6	District of Columbia	729
7	Louisiana	649
8	Michigan	603
9	Illinois	516
10	Pennsylvania	492
11	Maryland	487
12	Delaware	434
13	Indiana	361
14	Mississippi	300
15	Colorado	278
16	Minnesota	236
17	New Hampshire	235
18	Georgia	231
19	Ohio	220
20	Iowa	209
21	New Mexico	209
22	Virginia	181
23	Arizona	165
24	Washington	160
25	Alabama	157
26	Nevada	151
27	Missouri	145
28	Florida	136
29	California	129
30	Wisconsin	119
31	Kentucky	117
32	Nebraska	116
33	South Carolina	116
34	North Carolina	110
35	North Dakota	101
36	Oklahoma	90
37	Vermont	88
38	Kansas	84
39	South Dakota	84
40	Maine	75
41	Tennessee	69
42	Texas	69
43	Arkansas	60
44	Idaho	49
45	West Virginia	49
46	Puerto Rico	46
47	Utah	44
48	Oregon	42
49	Wyoming	31
50	Montana	17
51	Alaska	13
52	Hawaii	12

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	7
2	Massachusetts	5
3	Rhode Island	5
4	Connecticut	4
5	District of Columbia	4
6	Delaware	3
7	Arizona	2
8	Georgia	2
9	Maryland	2
10	New Mexico	2
11	New York	2
12	Pennsylvania	2
13	California	1
14	Illinois	1
15	Indiana	1
16	Iowa	1
17	Louisiana	1
18	Michigan	1
19	Minnesota	1
20	Mississippi	1
21	New Hampshire	1
22	Ohio	1
23	Alabama	0
24	Alaska	0
25	Arkansas	0
26	Colorado	0
27	Florida	0
28	Hawaii	0
29	Idaho	0
30	Kansas	0
31	Kentucky	0
32	Maine	0
33	Missouri	0
34	Montana	0
35	Nebraska	0
36	Nevada	0
37	North Carolina	0
38	North Dakota	0
39	Oklahoma	0
40	Oregon	0
41	Puerto Rico	0
42	South Carolina	0
43	South Dakota	0
44	Tennessee	0
45	Texas	0
46	Utah	0
47	Vermont	0
48	Virginia	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	129,210	1	99
Lake	Tennessee	98,347	2	99
Dakota	Nebraska	86,937	3	99
Buena Vista	Iowa	81,295	4	99
Lincoln	Arkansas	76,397	5	99
Davidson	Tennessee	10,351	235	92
Richland	South Carolina	5,429	587	81
Orange	California	2,758	1123	64
Pierce	Washington	2,691	1152	63
York	South Carolina	2,317	1292	58

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,547	1	99
Randolph	Georgia	3,246	2	99
Terrell	Georgia	3,048	3	99
Early	Georgia	3,042	4	99
Northampton	Virginia	2,306	5	99
Richland	South Carolina	173	655	79
Davidson	Tennessee	127	795	74
Pierce	Washington	98	942	70
Orange	California	70	1122	64
York	South Carolina	36	1474	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.